

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class        a__    (       snake_case_				,						snake_case_      ):
			"""simple docstring"""







			@register_to_config
			def __init__(     self   ,   lowercase = 128   ,   lowercase = 256   ,   lowercase = 2000.0   ,   lowercase = 768   ,   lowercase = 12   ,   lowercase = 12   ,   lowercase = 64   ,   lowercase = 2048   ,   lowercase = 0.1   ,   )	->    Any:
							'''simple docstring'''






							super().__init__()

							A__          =      nn.Sequential(
							    nn.Linear(lowercase   ,   d_model * 4   ,   bias=lowercase							)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowercase							)   ,   nn.SiLU()   ,   )

							A__          =      nn.Embedding(lowercase   ,   lowercase							)
							A__          =      False

							A__          =      nn.Linear(lowercase   ,   lowercase   ,   bias=lowercase							)

							A__          =      nn.Dropout(p=lowercase							)

							A__          =      nn.ModuleList()
							for lyr_num in range(lowercase							):
											# FiLM conditional T5 decoder
											A__          =      DecoderLayer(d_model=lowercase   ,   d_kv=lowercase   ,   num_heads=lowercase   ,   d_ff=lowercase   ,   dropout_rate=lowercase							)
											self.decoders.append(lowercase							)

							A__          =      TaLayerNorm(lowercase							)

							A__          =      nn.Dropout(p=lowercase							)
							A__          =      nn.Linear(lowercase   ,   lowercase   ,   bias=lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase							)	->    Tuple:
							'''simple docstring'''






							A__          =      torch.mul(query_input.unsqueeze(-1							)   ,   key_input.unsqueeze(-2							)							)
							return mask.unsqueeze(-3							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Dict:
							'''simple docstring'''






							A__     ,					A__     ,					A__          =      decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							A__          =      get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype							)

							A__          =      self.conditioning_emb(lowercase							).unsqueeze(1							)

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							A__          =      decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							A__          =      torch.broadcast_to(
							    torch.arange(lowercase   ,   device=decoder_input_tokens.device							)   ,   (batch, seq_length)   ,   )

							A__          =      self.position_encoding(lowercase							)

							A__          =      self.continuous_inputs_projection(lowercase							)
							inputs += position_encodings
							A__          =      self.dropout(lowercase							)

							# decoder: No padding present.
							A__          =      torch.ones(
							    decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype							)

							# Translate encoding masks to encoder-decoder masks.
							A__          =      [(x, self.encoder_decoder_mask(lowercase   ,   lowercase							)) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							A__          =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1							)
							A__          =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1							)

							for lyr in self.decoders:
											A__          =      lyr(
											    lowercase   ,   conditioning_emb=lowercase   ,   encoder_hidden_states=lowercase   ,   encoder_attention_mask=lowercase   ,   )[0]

							A__          =      self.decoder_norm(lowercase							)
							A__          =      self.post_dropout(lowercase							)

							A__          =      self.spec_out(lowercase							)
							return spec_out

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase=1e-6							)	->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							A__          =      nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=lowercase   ,   d_kv=lowercase   ,   num_heads=lowercase   ,   dropout_rate=lowercase							)							)

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=lowercase   ,   d_kv=lowercase   ,   num_heads=lowercase   ,   dropout_rate=lowercase   ,   layer_norm_epsilon=lowercase   ,   )							)

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=lowercase   ,   d_ff=lowercase   ,   dropout_rate=lowercase   ,   layer_norm_epsilon=lowercase							)							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=None   ,   lowercase=None   ,   lowercase=None   ,   lowercase=None   ,   lowercase=None   ,   )	->    Optional[int]:
							'''simple docstring'''






							A__          =      self.layer[0](
							    lowercase   ,   conditioning_emb=lowercase   ,   attention_mask=lowercase   ,   )

							if encoder_hidden_states is not None:
											A__          =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1e10							).to(
											    encoder_hidden_states.dtype							)

											A__          =      self.layer[1](
											    lowercase   ,   key_value_states=lowercase   ,   attention_mask=lowercase   ,   )

							# Apply Film Conditional Feed Forward layer
							A__          =      self.layer[-1](lowercase   ,   lowercase							)

							return (hidden_states,)

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							A__          =      TaLayerNorm(lowercase							)
							A__          =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowercase							)
							A__          =      Attention(query_dim=lowercase   ,   heads=lowercase   ,   dim_head=lowercase   ,   out_bias=lowercase   ,   scale_qk=lowercase							)
							A__          =      nn.Dropout(lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=None   ,   lowercase=None   ,   )	->    List[Any]:
							'''simple docstring'''






							A__          =      self.layer_norm(lowercase							)

							if conditioning_emb is not None:
											A__          =      self.FiLMLayer(lowercase   ,   lowercase							)

							# Self-attention block
							A__          =      self.attention(lowercase							)

							A__          =      hidden_states + self.dropout(lowercase							)

							return hidden_states

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Optional[Any]:
							'''simple docstring'''






							super().__init__()
							A__          =      Attention(query_dim=lowercase   ,   heads=lowercase   ,   dim_head=lowercase   ,   out_bias=lowercase   ,   scale_qk=lowercase							)
							A__          =      TaLayerNorm(lowercase   ,   eps=lowercase							)
							A__          =      nn.Dropout(lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=None   ,   lowercase=None   ,   )	->    Optional[Any]:
							'''simple docstring'''






							A__          =      self.layer_norm(lowercase							)
							A__          =      self.attention(
							    lowercase   ,   encoder_hidden_states=lowercase   ,   attention_mask=attention_mask.squeeze(1							)   ,   )
							A__          =      hidden_states + self.dropout(lowercase							)
							return layer_output

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Dict:
							'''simple docstring'''






							super().__init__()
							A__          =      TaDenseGatedActDense(d_model=lowercase   ,   d_ff=lowercase   ,   dropout_rate=lowercase							)
							A__          =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowercase							)
							A__          =      TaLayerNorm(lowercase   ,   eps=lowercase							)
							A__          =      nn.Dropout(lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=None							)	->    List[Any]:
							'''simple docstring'''






							A__          =      self.layer_norm(lowercase							)
							if conditioning_emb is not None:
											A__          =      self.film(lowercase   ,   lowercase							)

							A__          =      self.DenseReluDense(lowercase							)
							A__          =      hidden_states + self.dropout(lowercase							)
							return hidden_states

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							A__          =      nn.Linear(lowercase   ,   lowercase   ,   bias=lowercase							)
							A__          =      nn.Linear(lowercase   ,   lowercase   ,   bias=lowercase							)
							A__          =      nn.Linear(lowercase   ,   lowercase   ,   bias=lowercase							)
							A__          =      nn.Dropout(lowercase							)
							A__          =      NewGELUActivation()


			def   UpperCamelCase		(     self   ,   lowercase							)	->    Optional[Any]:
							'''simple docstring'''






							A__          =      self.act(self.wi_a(lowercase							)							)
							A__          =      self.wi_a(lowercase							)
							A__          =      hidden_gelu * hidden_linear
							A__          =      self.dropout(lowercase							)

							A__          =      self.wo(lowercase							)
							return hidden_states

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase=1e-6							)	->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							A__          =      nn.Parameter(torch.ones(lowercase							)							)
							A__          =      eps


			def   UpperCamelCase		(     self   ,   lowercase							)	->    Optional[Any]:
							'''simple docstring'''






							A__          =      hidden_states.to(torch.floataa							).pow(2							).mean(-1   ,   keepdim=lowercase							)
							A__          =      hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
											A__          =      hidden_states.to(self.weight.dtype							)

							return self.weight * hidden_states

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def   UpperCamelCase		(     self   ,   lowercase							)	->    Dict:
							'''simple docstring'''






							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.04_4715 * torch.pow(lowercase   ,   3.0							))							))

class        a__    (       nn.Module      ):
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase							)	->    List[str]:
							'''simple docstring'''






							super().__init__()
							A__          =      nn.Linear(lowercase   ,   out_features * 2   ,   bias=lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase							)	->    int:
							'''simple docstring'''






							A__          =      self.scale_bias(lowercase							)
							A__     ,					A__          =      torch.chunk(lowercase   ,   2   ,   -1							)
							A__          =      x * (1 + scale) + shift
							return x


"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
